There exist numerous data aggregation systems in which event-based data is captured from random sources and processes, and made available for use by third party data consumption processes. For example, event-based data may comprise automated or manually generated feedback or metadata associated with some event. An event may for example include any interaction between two or more nodes in some pre-defined space.
In many data aggregation systems, such inputted data is obtained from unknown or unqualified sources. Accordingly, the reliability and credibility of the event-based data being made available cannot be guaranteed. While some suspect data may be identified and/or filtered out by various mechanisms, it is impossible to identify and filter all such instances.
One further approach to enhancing the reliability of such event-based data is to identify and vet sources that are providing information to the data aggregation system. Unfortunately, because of the unpredictable nature of event-based data, i.e., who, what, when and how it is created, it is extremely difficult to identify such sources.